J Neurooncol (2006) 80:261–274 DOI 10.1007/s11060-006-9191-4
L A B O RA T O RY I N V E S T I G A T I O N
Increased expression of thymidylate synthetase (TS), ubiquitin specific protease 10 (USP10) and survivin is associated with poor survival in glioblastoma multiforme (GBM) Jessica M. Grunda Æ L. Burton Nabors Æ Cheryl A. Palmer Æ David C. Chhieng Æ Adam Steg Æ Tom Mikkelsen Æ Robert B. Diasio Æ Kui Zhang Æ David Allison Æ William E. Grizzle Æ Wenquan Wang Æ G. Yancey Gillespie Æ Martin R. Johnson
Received: 30 January 2006 / Accepted: 1 May 2006 / Published online: 14 June 2006 Springer Science+Business Media B.V. 2006
Abstract Background The limited success of empirically designed treatment paradigms for patients diagnosed with glioblastoma multiforme (GBM) emphasizes the need for rationally designed treatment strategies based on the molecular profile of tumor samples and their correlation to clinical parameters. Methods In the current study, we utilize a novel realtime quantitative low density array (RTQ-LDA) to identify differentially expressed genes in de novo GBM tissues obtained from patients with distinctly different clinical outcomes. Total RNA was isolated from a cohort of 21 GBM specimens obtained from patients with either good (long-term survival (LTS) >36 months post surgery, n = 8) or poor (died of the disease (DOD)
< 24 months post surgery, n = 13) prognosis. Nonneoplastic brain tissue (n = 5) was obtained from patients who underwent surgery for refractory epilepsy. Demographic data was assessed for correlation with survival using Cox proportional hazards models. Sufficient RNA was available to use RTQ-LDA to quantify the expression of 93 independent genes in 5 LTS, 4 DOD, and 5 non-neoplastic brain samples. The eight differentially expressed genes identified by RTQ-LDA in LTS versus DOD (P £ 0.050) were subsequently quantified in all 21 GBM samples by realtime quantitative PCR (RTQ). Results A correlation between younger patients and good prognosis was demonstrated (P £ 0.05). The combination of RTQ-LDA and RTQ identified thymidylate synthetase (TS), ubiquitin specific protease 10
J. M. Grunda Æ A. Steg Æ R. B. Diasio Æ M. R. Johnson Departments of Pharmacology and Toxicology, Division of Clinical Pharmacology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
K. Zhang Æ D. Allison Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
L. B. Nabors Department of Neurology, Division of Neuro-oncology, University of Alabama at Birmingham, Birmingham, AL 35294, USA G. Y. Gillespie Department of Surgery, Division of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA D. C. Chhieng Æ W. E. Grizzle Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
W. Wang Department of Biostatistics, Biostatistics and Bioinformatics Unit, University of Alabama at Birmingham, Birmingham, AL 35294, USA T. Mikkelsen Department of Neurosurgery, Henry Ford Hospital, Brain Tumor Center, Detroit, MI, USA M. R. Johnson (&) Department of Clinical Pharmacology, University of Alabama at Birmingham, 1824 6th Avenue South, Wallace Tumor Institute, Room 620, Birmingham, AL 35294-3300, USA e-mail:
[email protected]
C. A. Palmer Department of Pathology, Division of Neuropathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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(USP10), and survivin as significantly over-expressed (P £ 0.050) in DOD compared to LTS patients. Ribonucleotide reductase subunit M2 (RRM2) was identified as tumor-specific, but not associated with survival. Conclusions Taken collectively, TS, USP10, survivin and RRM2 may be useful as prognostic indicators and/ or in the development of rationally designed treatment protocols. Keywords Glioblastoma multiforme Æ Glioma Æ Low density array Æ Real-time quantitative PCR Æ Ribonucleotide reductase subunit M2 Æ Survival Æ Survivin Æ Thymidylate synthetase Æ Ubiquitin specific protease 10
Introduction Glioblastoma multiforme (GBM) remains the most common and lethal primary brain tumor with the average patient surviving 9–12 months, and fewer than 2% surviving over 5 years [1]. The common recurrence of GBM following neurosurgical resection is partly due to its infiltrative nature, making complete removal of tumor tissue virtually impossible [2]. In addition, GBM is known for its marked resistance to treatment with radiation and chemotherapy. Despite improved diagnostic tests and highly aggressive treatment regimens, median overall survival remains poor in comparison to other types of cancer [3]. Taken collectively, the failure to empirically develop an effective treatment for GBM emphasizes the need to utilize the molecular profile of tumor samples and its correlation to clinical parameters to develop rationally designed treatment strategies. Several studies have focused on using GBM gene expression profiles to identify potential new avenues for diagnosis, prognosis, staging, and therapy [4, 5]. Most of these studies have applied a focused candidate gene approach, which examines the expression of only one to several genes at a time. However, evidence from microarray analysis of gliomas suggests that examination of single genes offers limited information with poor clinical outcome correlations, since many genes act collectively and must be examined as a group [6, 7]. In fact, recent studies suggest that gene expression-based classification of malignant gliomas may correlate better with survival than histological classification [8] and is useful for the identification of previously unrecognized, clinically relevant prognostic indicators [9]. Unfortunately, the lethality of this disease has limited the availability of long-term survivor GBM specimens such that most multivariate studies have focused on gene
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expression differences between grades and types, or in progression of disease, not in clinical outcome [6, 7, 10]. In the current study, we utilize a progressive strategy of real-time quantitative low density array (RTQ-LDA) and real-time quantitative PCR (RTQ) to examine differences in gene expression between GBM specimens from patients with dramatically different clinical outcomes. Non-neoplastic brain was included in this study to discriminate between tumor-specific genes and those that are expressed in uninvolved tissue, a prerequisite in the future design of more specific anti-cancer immunoor chemotherapy. Genes examined encompass several functional groups including anti-apoptotic, pro-apoptotic, angiogenic, DNA repair, methylation, ubiquitin, transcription factor, and kinase and drug metabolism enzymes. Results from this study identified several potential prognostic indicators for clinical outcome that could also be used in the rational design of less toxic and ultimately more efficacious therapy protocols.
Materials and methods Tissue specimens This retrospective study was conducted using 19 surgically resected snap frozen de novo GBM specimens obtained from the University of Alabama at Birmingham (UAB, Birmingham, AL) Brain Tumor SPORE Tissue Core Facility (n = 15) and the Hermelin Brain Tumor Center at Henry Ford Hospital (Detroit, MI) (n = 4). In addition, three paraffin embedded GBM sections were obtained through the UAB Neuropathology division. One of these paraffin embedded sections was matched with one of the snap frozen samples (ultimately resulting in 21 unique GBM patient samples). In this study, good prognosis was defined as long-term survival (LTS) >36 months post surgery (n = 8) (the LTS time most often cited in the literature [11–16]), and poor prognosis as dead of disease (DOD) < 24 months post surgery, (n = 13). Survival was calculated as time of tumor resection to time of death or last follow-up. All non-neoplastic brain tissues (n = 5) were obtained from patients following surgery for control of refractory epilepsy through the UAB Brain Tumor SPORE Tissue Core Facility. Prior to molecular analysis, all GBM tissues were histologically examined and classified according to World Health Organization guidelines as grade IV (without infiltrative margins) by a neuropathologist. All studies using human tissues were approved by and conducted in accordance with the policies of the Institutional Review Board at UAB.
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RNA processing
RTQ-LDA analysis
Flash frozen tissues
PCR amplification was performed using the Applied Biosystems Prism 7900HT sequence detection system, as previously described by our lab [19]. Gene expression levels were calculated using the comparative cycle threshold (Ct) method [18]. RNA isolated from the non-neoplastic brain specimen (Table 1, patient 2) was used as the normalizer (assigned a relative expression value of 1.00) for each gene examined.
Total RNA was isolated from frozen tissues using Qiagen columns (QIAGEN, Valencia, CA) according to manufacturer’s instructions, and subsequently quantified for concentration and purity spectrophotometrically. Total RNA was diluted in RNase-free water containing 12.5 ng/ll of total yeast RNA (Ambion, Austin, TX) as a carrier and stored at –80C until analysis. Paraffin embedded tissues RNA was extracted from paraffin sections using the Roche High Pure RNA Paraffin Kit (Roche Diagnostics, Mannheim, Germany) as per the manufacturers instructions. RNA concentration was quantified by RTQ using the S9 housekeeping gene, which has previously been validated as ubiquitously expressed in non-neoplastic brain and GBM by our laboratory [17, 18]. cDNA synthesis RNA was reverse transcribed using the cDNA Archive Kit (Applied Biosystems, Foster City, CA) following manufacturer’s instructions. The yield of cDNA was subsequently quantified by RTQ using the S9 housekeeping gene. Real-time quantitative low-density array (RTQ-LDA) Plate design In this study, the RTQ-LDA card (Applied Biosystems) was configured into four identical 96 gene sets (Format 96a, P/N 4342259) so a total of 4 samples could be analyzed simultaneously. The genes selected for inclusion on the RTQ-LDA were selected based on the known function of each gene and their association with tumor progression, apoptosis, drug response, or patient survival. Three housekeeping genes, RPLPO, GAPDH and 18S were also included on each card. The total number of genes examined was based on the amount of tissue available. Amplification of total RNA was not performed since previous studies from our laboratory demonstrated an altered molecular profile in amplified samples [19].
RTQ Primer/probe sets for XRCC2, DNA (cytosine-5)methyltransferase 1 (DNMT1), CTP Synthase (CTPS), Ubiquitin specific protease 10 (USP10), XPA, and FLT1 (VEGF R1) were obtained from Applied Biosystems (Foster City, CA). The survivin and TS primer and probe sets were designed using Primer Express Software and optimized as previously described [17, 18]. All RTQ reactions, including no transcription controls for each target, were performed in triplicate. Gene expression levels were calculated by the relative standard curve method, using nonneoplastic brain RNA (Ambion, Austin, TX) as the standard [17, 18]. Immunohistochemistry Paraffin embedded tissues were cut into 5-lm sections, mounted to slides, and then deparaffinized and rehydrated with xylene and graded alcohol baths. Antigen retrieval was achieved by heating the slides in a 0.01 M EDTA pH 8 solution for 5 min in a pressure cooker, followed by treatment with 3% peroxide to quench endogenous peroxidases. For the primary block, slides were incubated with 3% goat serum for 20 min followed by a secondary biotin block. The slides were then incubated for 1 h at room temperature with primary antibodies to either Survivin (0.3 lg ml–1, Novus Biolognicals), TS (1.0 lg ml–1, Neomarkers, clone TS 106), or USP10 (2.5 lg ml–1, Abgent). A biotinylated mixture of goat anti-mouse/ rabbit was applied for 20 min as the secondary antibody. The slides were then incubated with Streptavidin peroxidase for 20 min to label the antibodies (Signet Ultrastreptavidin detection system #2254), for which the substrate 3-3¢diaminobenzidine (BioGenex, HK-153-5K) was used, and subsequently counterstained with hematoxylin. Matched sections processed identically and stained without primary antibody served as controls.
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264 Table 1 Patient demographic and clinical characteristic
*C, Caucasian; B, black; U, unknown; +, treated; –, Untreated; XRT, radiation; BCNU, carmustine; CCNU, lomustine; PCB, procarbonize; TMX, tamoxifen; TMZ, temozolomide
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Patient
Age (year)
Non-neoplastic 1 47 2 44 3 36 4 48 5 11 Average 37.2 LTS GBM 6 26 7 50 8 44 9 42 10 47 11 53 12 54 13 54 Average 46.3 DOD GBM 14 71 15 38 16 52 17 69 18 17 19 76 20 46 21 55 22 47 23 75 24 57 25 63 26 47 Average 55.6
Race*
Gender
Treatment XRT
BUCN
CCNU
PCB
TMX
TMZ
C C C C C
F F M F M
C C B C C C C C
F F M F M F M M
125 40 72 82 55 49 150 61 79.25
+ + + + + + + +
+ – – – + – + –
– – + – – + – –
+ – + – – + – –
– – + – – + – –
– + – + + + – +
C C C C C C B C U C C C C
M F F M F M F F M F M M M
12.7 21.3 4.8 1 16.7 1.3 6.3 0.3 12.3 2.6 7.8 3.8 3.7 7.3
+ + + – + – + – + – + + –
– – – – – – – – – – – – –
– – – – – – – – – – + – –
– – – – – – – – – – – – –
– – – – – – – – – – + – –
+ + – – + – + – + – + + +
Statistical analysis Correlation of age, gender, and treatment with survival To determine whether the survival time of patients is related to gender, a Welch’s t-test and log-rank test [20] were conducted. The demographic data also contains the age covariate, and since differences in survival between the groups may reflect or be masked by differences in age, parametric models (exponential, Weibull, and log-logistic) were used to test if the coefficients of covariates were significantly different from 0 [21]. To determine if patient treatment was associated with survival, a multivariate method that uses exponential distribution was conducted. RTQ-LDA The reliability of RTQ-LDA was determined by analyzing control cDNA on three separate
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Survival (months)
LDA-RTQ cards and a pair-wise statistical analysis was conducted using Pearson’s correlation coefficient [22]. To determine whether differences in gene expression were statistically significant, a mixture model approach was used [23, 24]. Briefly, the P-value for each gene on the RTQ-LDA was calculated using delta Ct values utilizing Welch’s t-test, which allows for unequal variances across two groups. Mixtures of Beta distributions were fitted to the P-values and a formal significance test for the overall difference in gene expression was performed. For each individual gene, a posterior probability (PTP) that the gene is differentially expressed across the two or more conditions analyzed was estimated on the basis of a fitted model and the data (Bayesian model). PTP is an estimate of the proportion of genes that are truly differentially expressed among the genes with an observed P-value smaller than the observed P-value of this particular gene [22]. For each gene, its corresponding fold change, P-value,
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and PTP value were calculated for four different group comparisons (DOD versus LTS, non-neoplastic versus GBM, non-neoplastic versus LTS, and nonneoplastic versus DOD). The small sample size in this study affects the type II error rate and reduces power such that not all of the genes that are truly differentially expressed may be detected. However, once a gene is found to be differentially expressed using a significance value of 0.05 (the type I error rate), the statistical significance is established and should not change with sample size. To determine if the expression levels of a set of genes could distinguish DOD and LTS, or non-neoplastic and tumor cases the data was analyzed using a supervised classification approach. One case was selected as the testing sample and remaining cases were used as the training set. A subset of genes was selected on the basis of the training cases utilizing a modified stepwise discriminant analysis [25]. To avoid overfitting of the data five genes were selected. Based on the criterion established from the training cases, each test sample was classified into one of the two groups (LTS or DOD; non-neoplastic or tumor) using Fisher’s discriminant function analysis [26]. These steps were repeated until each case was selected as the testing case. This sequence of steps ensures that an estimate of the ability of the derived function to predict the status of future cases is not upwardly biased [27]. To determine if the expression levels of survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGF R1 could distinguish the DOD and LTS cases, the gene expression data from 12 independent cases was analyzed using the above described supervised classification analysis with the following exception; the discrimination analysis was performed using each individual gene, the three genes with the smallest P-values, or a subset of genes selected from the eight genes [25–27].
RTQ A Welch’s t-test was conducted to determine the unequal variance across the LTS and DOD groups for the survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGF R1 genes. The mean expression, value of test statistics, degree of freedom of the test, and the P-value for the eight genes, were calculated. The association between the expression of genes (survivin, TS, USP10) and survival was evaluated by parametric multivariate regression using exponential distribution, adjusted for age.
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Results Patients Correlation of patient demographics with survival Table 1 summarizes the demographic characteristics and clinical treatment for the patients examined in this study. The 5 patients with epilepsy ranged from 11 years to 48 years in age, were almost equally male (n = 2) and female (n = 3), and were all Caucasians. The 8 GBM good prognosis LTS patients survived an average of 79.25 months (median = 66.5 months) and ranged from 26 years to 54 years in age (average = 46.3 years). In contrast, the 13 GBM poor prognosis DOD patients survived an average of 7.2 months (median = 4.8 months) and were on average older than the LTS patients (average = 55.6 years, range = 17–76 years). Both GBM patient prognosis groups consisted mainly of Caucasians and had an equal distribution of males and females. Neither the Welch’s t-test nor the log-rank test indicated a correlation between survival and gender (P = 0.8237 and 0.843, respectively). The parametric tests (including the exponential, Weibull, and log-logistic parametric distribution models) also agreed that gender did not correlate with survival (P > 0.05). However, when the age covariate was examined, a marginally statistical correlation was observed between younger age and good prognosis (P < 0.05). As shown in Table 1, treatment varied between the LTS and DOD groups with the LTS patients receiving more treatment than the DOD patients. Unlike the LTS group no patients in the DOD group received carmustine (BCNU), or procarbozine (PCB). In addition patients 17, 19, 21, and 23 of the DOD group died before any treatment could be administered. Statistical analysis determined radiation (P < 0.0001) and BCNU (P = 0.0276) therapy are significantly correlated with increased patient survival. RTQ-LDA analysis Inter- and intra-assay variation To determine the inter-assay variation, control cDNA was examined on three separate RTQ-LDA cards which were prepared and examined over 3 days. Pairwise statistical analysis determined a correlation coefficient of 0.98 between all cards. Similar variability was obtained in a separate study examining 48 genes involved in the WNT and Hedgehog pathways [19]. Analysis of 5 separate tissue samples (4 replicates for
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each sample) indicated an intra-assay coefficient of variation (CV) of approximately 5% [19]. Correlation of gene expression in matched frozen and paraffin embedded tissues To determine whether RTQ-LDA is suitable for quantifying gene expression in archival formalin-fixed paraffin-embedded (FPE) tissues, we examined the correlation of 96 genes in RNA obtained from matched frozen versus FPE. As shown in Fig. 1, a two-dimensional plot depicting Ct values from a frozen (abscissa) and matching FPE (ordinate) GBM sample from patient number 7 (see Table 1) demonstrates a significant (P < 0.0001) linear correlation (r2 = 0.91) with a Pearson correlation coefficient (r) of 0.95. Similar results (r2 = 0.96, r = 0.98) were obtained when examining a matched frozen and paraffin embedded glia cell line (data not shown).
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the remaining 85 genes examined were not found to be statistically significant using the cut-off P-value of 0.05 (Table 2, bottom panel). When supervised classification analysis was performed to determine if 5 selected genes could distinguish the DOD and LTS samples, prediction was significantly better than chance levels (P = 0.048, Fisher’s exact test). In all cases, survivin was selected as the most significant gene to distinguish the DOD and LTS cases. When the expression levels of the 8 selected genes (survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGF R1) were analyzed, using each individual gene, the 3 genes with the smallest P-value, or a subset of the 8 genes, the resulting predictions were not significantly better than chance (P > 0.05). Gene expression differences between non-neoplastic brain and GBM
RTQ-LDA expression analysis of 93 genes was conducted on total RNA obtained from 5 LTS (patients 6–10) and 4 DOD (patients 14–17) GBM samples (Table 2). To reflect the biological variation, the average gene expression values for both LTS and DOD tissues are shown in Table 2 ± the standard error over all the samples analyzed. Based on these averages, survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGF R1 genes demonstrated significant (P £ 0.05) over-expression in DOD versus LTS GBM specimens (Table 2, top panel). The corresponding cut-off PTP value was 0.594, indicating that about 5 of 8 genes were expected to be differentially expressed. Differences in expression levels of
RTQ-LDA analysis for the 93 genes examined in Table 2 was also conducted on non-neoplastic brain (Table 1, patients 1–5). Comparative analysis of the 8 genes identified as significantly different between LTS versus DOD (Table 2, top panel) and non-neoplastic brain are shown in Table 3 (top panel). The survivin and VEGF R1 genes were significantly over-expressed in both LTS and DOD tissues compared to non-neoplastic brain (P < 0.05). XRCC2 was also over-expressed in both the LTS and DOD tissues, but statistical significance was only reached in the DOD tissues. The DNMT1, CTPS, USP10, and XPA genes were expressed lower in both LTS and DOD tissues compared to non-neoplastic brain, but these data only reached statistical significance in LTS tissues. Of particular interest, TS was significantly over-expressed (P < 0.05) in DOD but under-expressed in LTS compared to non-neoplastic brain (although the LTS
Fig. 1 To determine if the real-time quantitative low density array is appropriate for paraffin embedded tissue gene expression analysis, the expression of 93 genes were quantified in a matched flash frozen and paraffin embedded GBM sample. A correlative
plot of Ct values obtained from the flash frozen (y-axis) versus the matched paraffin embedded (x-axis) sample demonstrated a coefficient of determination of 0.91 and a Pearson correlation coefficient of 0.95 over the 93 genes examined
Gene expression differences between LTS and DOD GBM tissues
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Table 2 Gene expression differences between LTS and DOD tissues Gene Significant difference in gene expression Survivin (BIRC5) DNA methyltransferase 1 (DNMT1) Thymidylate synthetase (TS) VEGF R1 (FLT1) CTP synthase (CTPS) XRCC2 Ubiquitin specific protease 10 (USP1 0) XPA No significant difference in gene expression PDK1 (PDPK1) Ribonucleotide reductase subunit M1 (RRM1) BRMS1 Cytochrome-C (CYC1) XRCC3 XRCC1 BRCA1 iNOS (NOS2A) ERCC1 dCMP deaminase (DCTD) MDM2 cyclin D1 (CCND1) SMAC/Diablo (SMAC) siah-1 (SIAH1) FAD D RASSF1 Fas ligand (TNFSF6) Sprouty 4 (SPRY4) BAD Thrombospondin-2 (THBS2) DNA methyltransferase 3 alpha (DNMT3A) XPB (ERCC3) p27, Kip1 (CDKN1 B) EGFR XPD (ERCC2) BAX Ang-2 (ANGPT2) PCNA CRSP3 VEGF R3 (FLT4) Apaf-1 (APAF1) XPC XPF (ERCC4) DNA methyltransferase 3 beta (DNMT3B) VEGF A (VEGF) Sprouty 2 (SPRY2) Ang-1 (ANGPT1) VEGFC VEGFB Ubiquitin hydrolase-5 (UCHL5) Caspase-3 (CASP3) COX-2 (PTGS2) XPG (ERCC5) APC c-Myc (MYC) cIAP-2 (BIRC3) Ubiquitin thiolesterase-1 (UCHL1) FLIP (CFLAR) TXNIP Hypoxia-inducible factor 1-alpha (HIF1A)
GBM LTS (n = 5)a
GBM DOD (n = 4)b
Fold change in DODc
P-valued
PTP restrictede
7.628 0.333 0.636 0.952 0.236 0.677 0.292 0.229
± ± ± ± ± ± ± ±
0.787 0.054 0.176 0.064 0.025 0.291 0.040 0.021
29.818 0.777 1.919 1.308 0.477 1.784 0.570 0.353
± ± ± ± ± ± ± ±
6.369 0.075 0.336 0.108 0.086 0.399 0.114 0.045
3.909 2.333 3.017 1.374 2.021 2.635 1.952 1.541
› › › › › › › ›
0.004 0.004 0.015 0.024 0.026 0.033 0.036 0.037
0.632 0.631 0.611 0.602 0.601 0.596 0.594 0.594
0.147 0.341 0.441 0.170 0.363 0.203 0.369 0.281 0.424 0.273 0.592 0.207 0.257 0.306 0.669 0.320 0.845 0.403 0.175 0.450 0.211 0.197 0.226 14.275 0.277 0.667 0.613 0.675 0.358 0.521 0.223 0.287 0.329 0.542 3.942 0.844 0.473 0.639 0.255 0.100 0.601 0.690 0.207 0.085 3.671 1.123 0.062 0.267 0.590 1.021
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.031 0.059 0.061 0.027 0.073 0.107 0.091 0.179 0.075 0.027 0.163 0.073 0.040 0.073 0.124 0.071 0.368 0.116 0.048 0.083 0.033 0.037 0.069 7.967 0.055 0.097 0.261 0.164 0.057 0.146 0.046 0.056 0.067 0.102 1.726 0.401 0.122 0.283 0.085 0.029 0.190 0.255 0.046 0.021 1.227 0.828 0.012 0.065 0.173 0.383
0.255 0.576 0.672 0.276 0.668 0.850 0.765 2.752 0.558 0.422 6.189 0.666 0.390 0.420 0.886 0.534 0.707 0.938 0.312 0.631 0.274 0.303 0.326 34.778 0.408 0.842 0.901 1.246 0.473 0.744 0.346 0.393 0.476 0.854 11.224 0.906 0.819 0.112 0.481 0.137 0.968 2.327 0.259 0.106 3.893 0.278 0.069 0.330 0.977 1.246
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
0.028 0.095 0.100 0.048 0.158 0.273 0.228 1.558 0.068 0.080 3.358 0.311 0.077 0.064 0.099 0.110 0.442 0.333 0.078 0.104 0.042 0.068 0.062 21.197 0.088 0.99 0.252 0.451 0.085 0.170 0.098 0.087 0.114 0.208 6.688 0.149 0.334 0.112 0.174 0.035 0.407 1.723 0.053 0.025 0.574 0.122 0.036 0.088 0.465 0.336
1.735 1.689 1.524 1.624 1.840 4.187 2.073 9.794 1.316 1.546 10.454 3.217 1.517 1.374 1.325 1.668 1.196 2.326 1.786 1.401 1.296 1.537 1.443 2.436 1.473 1.262 1.470 1.846 1.319 1.427 1.554 1.372 1.447 1.575 2.848 1.073 1.732 5.711 1.885 1.369 1.611 3.372 1.250 1.247 1.060 4.036 1.115 1.238 1.657 1.220
› › › › › › › › › › › › › › › › fl › › › › › › › › › › › › › › › › › › › › fl › › › › › › › fl › › › ›
0.050 0.058 0.081 0.085 0.098 0.111 0.127 0.156 0.162 0.164 0.168 0.168 0.173 0.192 0.199 0.199 0.202 0.215 0.218 0.220 0.223 0.226 0.228 0.236 0.239 0.251 0.254 0.274 0.280 0.315 0.340 0.352 0.354 0.363 0.380 0.391 0.392 0.395 0.400 0.400 0.408 0.437 0.441 0.454 0.506 0.507 0.523 0.541 0.550 0.555
0.588 0.584 0.576 0.575 0.571 0.567 0.563 0.555 0.554 0.553 0.552 0.552 0.551 0.547 0.545 0.545 0.545 0.542 0.541 0.540 0.540 0.539 0.539 0.537 0.536 0.534 0.533 0.529 0.528 0.520 0.515 0.513 0.512 0.510 0.507 0.504 0.504 0.504 0.503 0.503 0.501 0.495 0.494 0.492 0.481 0.481 0.477 0.474 0.472 0.471
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Table 2 continued Gene
GBM LTS (n = 5)a
GBM DOD (n = 4)b
Fold change in DODc
p1 6 Ink4A (CDKN2A) SRC Protein kinase C, delta (PRKCD) Carboxylesterase-1 (CES1) TRAIL-R4 (TNFRSF1 OD) TERT Cytochrome P450 3A5 (CYP3A5) Ubiquitin specific protease 16 (USP1 6) Caspase-8 (CASP8) Ubiquitin thiolesterase-3 (UCHL3) Ribonucleotide reductase subunit M2 (RRM2) Ubiquitin specific protease 8 (USP8) Deoxycitidine kinase (DCK) DNA methyltransferase 2 (DNMT2) Crystallin-alpha B (CRYAB) Livin (BIRC7) DR-5 (TNFRSF10B) VEGF R2 (KDR) Protein kinase C, mu (PRKCM) XRCC4 Thymidine phosphorylase (ECGF1) DR-4 (TNFRSF10A) FAP-1 (PTPN13) Bcl-XL (BCL2L1) TRAIL-R3 (TNFRSF I 0C) XRCC5 Fas receptor (TNFRSF6) Bcl-2 (BCL2) clAP-1 (BIRC2) PKB (PTK2B) ERCC6 NF-KB 2 (NFKB2) KISS 1 Crystallin-alpha A (CRYAA) VEGF D (FIGF)
3.356 0.291 0.092 1.143 0.468 0.900 0.977 0.217 0.695 0.116 40.866 0.406 0.215 0.236 0.594 0.798 1.820 0.479 0.488 0.226 1.012 0.318 0.495 0.505 0.560 0.261 0.654 0.571 0.353 0.082 0.170 0.769 0.000 0.000 0.000
5.196 0.425 0.109 1.680 0.580 3.298 0.884 0.264 0.652 0.117 69.896 0.410 0.222 0.245 1.000 2.256 1.681 0.517 0.524 0.250 1.258 0.324 0.482 0.472 0.561 0.267 0.559 0.499 0.337 0.087 0.162 0.699 0.000 0.000 0.000
1.548 1.464 1.179 1.470 1.239 3.665 1.105 1.213 1.067 1.014 1.710 1.009 1.031 1.040 1.683 2.827 1.083 1.079 1.074 1.106 1.243 1.020 1.026 1.069 1.002 1.025 1.171 1.143 1.049 1.059 1.051 1.099 * * *
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
1.479 0.053 0.017 0.691 0.160 0.402 0.553 0.058 0.227 0.038 11.006 0.104 0.043 0.060 0.404 0.385 0.550 0.142 0.079 0.048 0.205 0.055 0.144 0.144 0.231 0.059 0.267 0.150 0.076 0.028 0.034 0.293 0.000 0.000 0.000
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
4.892 0.140 0.024 0.865 0.205 1.317 0.547 0.076 0.095 0.027 25.675 0.042 0.024 0.040 0.524 1.622 0.237 0.123 0.101 0.060 0.757 0.117 0.092 0.077 0.244 0.068 0.194 0.091 0.053 0.033 0.028 0.232 0.000 0.000 0.000
› › › › › › fl › fl › › › › › › › fl › › › › › fl fl › › fl fl fl › fl fl
P-valued
PTP restrictede
0.556 0.556 0.584 0.586 0.589 0.626 0.647 0.656 0.659 0.672 0.683 0.683 0.705 0.706 0.714 0.718 0.739 0.753 0.768 0.776 0.806 0.833 0.870 0.885 0.898 0.900 0.901 0.904 0.968 0.968 0.977 0.998 * * *
0.470 0.470 0.465 0.464 0.464 0.456 0.451 0.449 0.449 0.446 0.444 0.444 0.439 0.439 0.437 0.436 0.431 0.428 0.425 0.423 0.416 0.411 0.402 0.399 0.396 0.395 0.395 0.394 0.379 0.379 0.377 0.372 * * *
a
Average gene expression of LTS GBM tissues
b
Average gene expression of DOD GBM tissues
c
Gene expression fold change in DOD relative to LTS tissues
d
P-value calculated from Welch’s t-test
e
PTP Restricted values calculated from Welch’s t-test
±, Standard error of mean; *, no expression detected; (›), increased expression in DOD; (fl), decreased expression in DOD
versus non-neoplastic brain expression levels did not reach statistical significance; P > 0.05). A total of 44 of the remaining 85 genes (52%) demonstrated statistically significant differences (P < 0.05) in expression between non-neoplastic brain and GBM (regardless of prognosis). When a more stringent significance cut-off of P £ 0.003 was applied, a total of 23 genes (26%) were identified as differentially expressed between non-neoplastic brain and GBM (Table 3, bottom panel). Interestingly, only ribonucleotide reductase subunit M2 (RRM2) demonstrated GBM specific expression with no detectable signal in non-neoplastic brain (Table 3, bottom panel). There was no significant difference in RRM2 expres-
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sion between LTS and DOD samples (Table 2). The remaining 22 genes were expressed lower in GBM relative to non-neoplastic brain tissue (Table 3, bottom panel). When GBM expression data was segregated into non-neoplastic versus LTS and non-neoplastic versus DOD, 18 (78%) and 11 (48%) genes were identified as differentially expressed, respectively (Table 3, bottom panel). Comparative analysis demonstrated that 10 (43%) of these genes were over- (RRM2), or under- (UCHL5, APC, ERCC5, PRKCD, PDK1, PKB, DCK, DNMT2, ERCC6) expressed in both LTS and DOD tissues compared to non-neoplastic brain (P < 0.003).
J Neurooncol (2006) 80:261–274 Table 3 Differences in gene expression between nonneoplastic and GBM tissues
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Gene
Non-neoplastic Tumour Fold changea
versus P-valueb
Non-neoplastic LTS Fold changec
versus P-value
Non-neoplastic DOD Fold changed
Genes significantly differentially expressed between LTS and DOD tissues Survivin 11.659 › < 0.001 5.085 › 0.002 19.876 DNMT 1 1.755 fl 0.055 2.794 fl 0.007 1.198 TS 1.143 › 0.914 1.660 fl 0.176 1.818 VEGF R1 2.185 › 0.016 1.874 › 0.040 2.575 CTPS 1.962 fl 0.018 2.854 fl 0.004 1.411 XRCC2 1.803 › 0.449 1.044 › 0.250 2.752 USP10 1.794 fl 0.040 2.554 fl 0.009 1.307 XPA 2.129 fl 0.073 2.643 fl 0.037 1.713
a
Fold change in tumour tissues
b P-values calculated from Welch’s t-test c
Fold change in LTS tissues
d
Fold change in DOD tissues
*, No expression detected; (›), increased gene expression; (fl), decreased gene expression
› fl › › fl › fl fl
Genes differentially expressed between non-neoplastic and GBM tissues (P < 0.003) RRM2 *› < 0.001 *› < 0.001 *› UCHL5 10.357 fl < 0.001 12.055 fl < 0.001 8.807 fl APC 7.807 fl < 0.001 8.664 fl 0.002 6.949 fl ERCC5 4.061 fl < 0.001 4.513 fl 0.002 3.610 fl UCHL3 4.332 fl < 0.001 4.359 fl 0.008 4.298 fl Apaf-1 2.855 fl < 0.001 3.558 fl 0.001 2.289 fl PRKCD 4.595 fl < 0.001 4.960 fl 0.001 4.209 fl CYC1 3.302 fl < 0.001 4.209 fl < 0.001 2.601 fl PDK1 4.655 fl < 0.001 6.181 fl < 0.001 3.557 fl PKB 14.737 fl < 0.001 15.125 fl 0.001 14.279 fl DCK 3.558 fl 0.001 3.607 fl 0.002 3.499 fl XRCC5 3.674 fl 0.001 3.715 fl 0.003 3.623 fl UCHL1 9.022 fl 0.001 9.482 fl < 0.001 8.506 fl USP16 4.394 fl 0.001 4.810 fl 0.005 3.966 fl ERCC4 3.132 fl 0.001 3.755 fl 0.002 2.594 fl XRCC4 4.313 fl 0.001 4.515 fl 0.002 4.084 fl ERCC3 3.720 fl 0.001 4.608 fl 0.001 2.998 fl SRC 3.525 fl 0.002 4.253 fl 0.002 2.904 fl DNMT2 4.720 fl 0.002 4.804 fl 0.002 4.619 fl ERCC6 5.336 fl 0.002 5.220 fl 0.002 5.488 fl 0.002 3.355 fl 0.007 2.172 fl DCTD 2.701 fl FAP-1 2.375 fl 0.003 2.348 fl 0.018 2.410 fl FLIP 3.114 fl 0.003 3.443 fl 0.007 2.782 fl
When 5 selected genes were used to distinguish the non-neoplastic and tumor samples using supervised classification analysis, prediction was significantly better than chance levels (P = 0.027, Fisher’s exact test). RRM2 was selected as the most significant gene to differentiate the non-neoplastic and tumor samples. RTQ analysis To confirm the RTQ-LDA results, RTQ was conducted on survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGFR1 genes in the original 5 LTS and 4 DOD GBM samples, as well as an additional 3 LTS and 9 DOD samples that had insufficient RNA for RTQ-LDA (Table 1). As shown in Table 4, the TS, USP10, and survivin genes maintained significant over-expression in DOD patients (P < 0.05). When gene expression was adjusted for the age covariate using parametric multivariate regression
versus P-value
< 0.001 0.614 0.045 0.006 0.197 0.040 0.336 0.184 < 0.001 0.001 < 0.001 0.003 0.001 0.042 0.002 0.005 0.003 0.003 0.001 0.005 0.071 0.017 0.034 0.007 0.014 0.047 0.002 0.002 0.026 0.015 0.021
analysis, survivin (P = 0.0258) and USP10 (P = 0.0017) remained statistically significant while TS (P = 0.0746) remained marginally significant. Immunohistochemistry Immunohistochemistry was performed on PET from LTS and DOD patients to confirm the RTQ results. Figure 2 displays representative stains from the immunohistochemical analysis. Survivin, TS, and USP10 demonstrated stronger staining in the DOD compared to the LTS tissues (Fig. 2, panels A–F).
Discussion The incremental progress made in empirically developing an effective treatment for patients with GBM has resulted in attempts to identify genes that
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Table 4 RTQ analysis of LTS and DOD tissues Gene
LTS (n = 8)a
Significant difference in gene expression TS 0.815 ± 0.146 USP10 0.261 ± 0.055 Survivin 13.430 ± 3.220 No Significant Difference in Gene Expression XRCC2 1.150 ± 0.319 XPA 1.651 ± 0.494 CTPS 0.678 ± 0.070 VEGFR1 0.874 ± 0.316 DNMT1 1.302 ± 0.214
DOD (n = 13)b
Foldc
P-valued
2.079 ± 0.306 0.476 ± 0.052 36.108 ± 8.556
2.552 › 1.822 › 2.689 ›
0.003 0.009 0.010
› fl fl fl ›
0.074 0.143 0.175 0.208 0.572
2.161 0.848 1.148 0.430 1.395
± ± ± ± ±
0.512 0.074 0.332 0.040 0.164
1.879 1.946 1.693 2.034 1.071
a
Average gene expression of LTS samples
b
Average gene expression of DOD samples
c
Fold change in DOD samples
d
P-Values calculated from Welch’s t-test
±, Standard error of mean; (›), increased expression; (fl), decreased expression
could, ultimately, be used to rationally design targeted treatment paradigms. In the current exploratory study, RTQ-LDA was utilized to quantify 93 genes in de novo GBM tissues obtained from patients with distinctly different clinical outcomes (LTS versus DOD). In addition, non-neoplastic brain was examined to identify tumor-associated genes regardless of prognosis. The high morbidity of GBM (only
Fig. 2 Immunohistochemical staining for survivin, TS, and USP10 on paraffin-embedded tissues from DOD and LTS patients. Immunohistochemical staining for survivin on a (A) DOD and (B) LTS tissue, TS on a (C) DOD and (D) LTS sample, and USP10 on a (E) DOD and (F) LTS sample
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1–2% of patients survive over 36 months [12, 15]) has limited the availability of tissue obtained from long-term survivors. In fact, careful review of patient records (at both this institution and the Henry Ford Hospital) from 1990 to 2002, identified only 8 LTS patients that were not lost to follow-up, had complete clinical data and sufficient remnant tissue available for molecular analysis. Although this small
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sample size reduced the power of the molecular analysis presented in this study (not all differentially expressed genes may have been detected), the statistical significance of the genes that were identified will not change with sample size. The recent introduction of RTQ-LDA represents a significant advance in our ability to simultaneously quantify multiple genes. Initial studies demonstrated RTQ-LDA has comparable intra-assay variability to RTQ (CV = 0.97). In addition, when the Ct values from 96 genes were determined in matched frozen and fixed, paraffin embedded GBM, a statistically significant correlation (r = 0.95; P = < 0.0001) was obtained (Fig. 1). Similar results were obtained from a more extensive study analyzing matched paraffin embedded and snap frozen ovarian biopsies, which is described in detail elsewhere [19]. Collectively, these data demonstrate that RTQ-LDA (which utilizes the same principles as RTQ) expands our capability to quantitatively analyze multiple genes from either snap frozen clinical resections or archival PET. In the current study, a total of 21 de novo Grade IV GBM specimens with available demographic, treatment, and survival data were obtained from the UAB Brain Tumor SPORE Tissue Core Facility and from the Hermelin Brain Tumor Center at Henry Ford Hospital (Table 1). Statistical analysis was performed to identify phenotypic and/or genotypic trends that correlate with survival. While no association was found between clinical outcome and either gender or race, a significant correlation was determined between younger patients and increased survival. This association has also been reported by several other investigators and may result from more aggressive treatment of younger patients, tissue heterogeneity altering clinical diagnosis, changing pathological diagnostic criteria, potential subtypes of GBM, or random events in small population studies [15, 28]. Statistical analysis of patient treatment and clinical outcome indicated an association between BCNU and survival. However, timing of BCNU therapy ranged from early after diagnosis to later stages of treatment, preventing a definitive conclusion from this data set. Radiation therapy was also found to be associated with increased patient survival. However, the four patients with the shortest survival times who also did not receive radiotherapy, died before any treatment could be administered, therefore this association may represent an artifact of the data. Due to the variability in clinical data it is not possible to distinguish whether the differences in gene expression identified in this study correlate to better response to treatment or to a molecular subtype of GBM (a problem also cited by other GBM investigators examining LTS [15]).
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To identify differentially expressed genes in tumor samples from patients with distinctly different clinical outcomes, RTQ-LDA analysis was performed on 5 LTS and 4 DOD tissues. Survivin, TS, XRCC2, DNMT1, CTPS, USP10, XPA, and VEGF R1 genes were determined to be differentially expressed (Table 2). In addition, supervised classification analysis indicated survivin is the strongest predictor of clinical outcome from the 93 genes analyzed. The 8 genes identified using RTQ-LDA were subsequently analyzed by RTQ in the original samples, as well as an additional 3 LTS and 9 DOD samples (where sufficient RNA for RTQ-LDA analysis was not available). As shown in Table 4, RTQ analysis of all 8 LTS and 13 DOD samples identified survivin, TS, and USP10 genes as significantly over-expressed in poor prognosis (DOD) patients. When statistical analysis was performed to adjust for the age covariate, survivin, TS, and USP10 remained significantly correlated with patient survival. In agreement with the RTQ results, immunohistochemical analysis demonstrated stronger staining of survivin, TS, and USP10 in the DOD compared to the LTS tissues (Fig. 2, panels A–F). Survivin expression has been associated with resistance to apoptosis, radio-, and chemotherapy, as well as increased metastatic potential and proliferation [29, 30]. In addition, increased survivin expression has been correlated with poor clinical outcome in multiple cancers including neuroblastoma, colorectal, breast, nonsmall-cell lung, and GBM [30, 31]. In the current study, comparative expression analysis of GBM (regardless of prognosis) and non-neoplastic brain determined little or no survivin expression in non-malignant tissues (see Table 3, top panel). Recent studies that specifically target survivin through anti-sense oligonucleotides, dominant negative mutants, ribozymes, small interfering RNAs, or cyclin dependent kinase inhibitors [32], or decrease its expression through treatment with resveratrol [33] and sulindac [34], demonstrate a reversal of survivin-mediated irradiation and drug resistance [32]. Collectively this data confirms previous work by other groups associating survivin with GBM patient outcome and suggests survivin may be both a prognostic indicator and potential tumor associated gene for targeted treatment paradigms. TS expression has also been associated with resistance to radio- and chemotherapy as well as increased proliferation [35, 36]. In addition, increased TS expression has been correlated with poor clinical outcome in numerous cancers including colorectal [37], bladder [38], and ovarian [39]. However, to our knowledge, this is the first report correlating elevated TS expression with poor survival in GBM patients
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(Tables 2 and 4). Comparative analysis of GBM (regardless of prognosis) and non-neoplastic brain determined no significant difference in TS expression (see Table 3, top panel). However, when GBM samples were segregated into LTS and DOD groups, differential TS expression became significant in the non-neoplastic versus DOD group (see Table 3, top panel). These analyses may, in part, explain the molecular basis behind recent successes treating CNS tumors with fluoropyrimidines (5-FU embedded microspheres and capecitabine) [40, 41]. These reports, combined with preclinical studies demonstrating synergistic anti-tumor efficacy with concomitant administration of irradiation (XRT) and capecitabine, provided the rational for an ongoing Phase I clinical trial examining capecitabine and XRT for the treatment of GBM at this institution [42–44]. Recent reports describe the development of a TS specific inhibitor (ZD9331) which, according to preliminary results, demonstrate significant anti-tumor efficacy [45]. Although this agent has not specifically been evaluated in GBM, the association between TS expression and GBM patient survival may provide the impetus for future clinical studies. Interestingly, elevated USP10 was also shown to correlate with poor GBM clinical outcome (Tables 2 and 4). Comparative analysis of GBM (regardless of prognosis) and non-neoplastic brain determined a significant difference in USP10 expression (see Table 3, top panel). However, when GBM samples were segregated into LTS and DOD groups, differential USP10 expression was only significant in the non-neoplastic versus LTS group (see Table 3, top panel). USP10 is an ubiquitin-specific protease (USP) responsible for the de-ubiquitination of proteins [46]. Ubiquitination is best known as the mechanism responsible for targeting proteins for degradation, however, recent studies suggest that ubiquitination also modifies the trafficking, half-life, and interaction of proteins [47]. USPs are postulated to play critical roles in embryogenesis and protein regulation [48, 49] and several studies have indicated their involvement in pro-apoptotic pathways [50], as well as cancer [51]. Recent studies describing anti-tumor efficacy following inhibition of ubiquitin ligases [52], and the proteasome [53] has resulted in the development of bortezomib (a proteasome inhibitor which is currently being evaluated in several clinical trials) [53]. Compared to survivin and TS, relatively little is known about the role of USP10 in the etiology of tumorigenesis. However, current research elucidating the importance of protein degradation as a regulatory mechanism in cancer, and the association identified between USP10 and GBM survival, offers
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the exciting possibility that some genes in this pathway may be both prognostic indicators and potential targets for specific treatment paradigms. Expression analysis of non-neoplastic brain was included in this study to identify tumor-associated genes (independent from those associated with clinical outcome) that could be used in the rational design of more specific therapeutic regimens. RRM2 was the only gene which demonstrated elevated expression in GBM with no detectable signal in non-neoplastic brain. In addition, supervised classification analysis identified RRM2 as the strongest predictor of non-neoplastic or tumor tissue type. RRM2 has been shown to be induced by activated E2F1 in cycling cells and may be an indicator of proliferating cells compared with relatively postmitotic non-neoplastic brain. RRM2 over-expression has also been correlated with resistance to radioand chemotherapy, and enhanced malignant potential in multiple cancers [54]. Therapies targeting or decreasing RRM2 expression, such as anti-sense cDNA [55], RRM2-specific siRNA [56] and Flavopiridol [57], have displayed a reversal of RRM2 mediated drug resistance and growth. The tumor-specific expression of RRM2 demonstrated in this study may provide a molecular basis for previous studies suggesting efficacy of RRM2 inhibitors in the treatment of GBM [55]. Arguably, the most dramatic therapeutic successes this century include the development of antibiotics and vaccines which target proteins exclusively expressed by infectious microbes and/or viruses. Understanding the molecular basis of response to these agents remains a central effort of laboratories worldwide and has, overall, provided a foundation for our current understanding of cell physiology. However, applying this successful paradigm for the development of specific anti-neoplastic strategies remains elusive since cancer cells originate from normal precursors and subsequently express most of the proteins found in normal cells. For the treatment of GBM, progress in increasing median patient survival has been measured in months instead of years (such as the recent introduction of temozolomide [58]). In the current study, we utilized a progressive strategy of RTQ-LDA and RTQ analysis to examine tissue samples of GBM patients with distinctly different clinical outcome. These analysis confirmed previous studies associating survivin with GBM patient outcome and identified TS and USP10 as potential prognostic indicators. Additionally, analysis of non-neoplastic tissues determined survivin and RRM2 as tumor-associated genes that could be targeted in the rational design of more specific therapeutic regimens. The low incidence of LTS in patients diagnosed with GBM emphasizes the need to utilize these results in a larger
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cohort of samples from cooperative groups such as NABTT (New Approaches to Brain Tissue Therapy, NABTT), to ultimately confirm the significance of these results and potentially distinguish whether these differentially expressed genes correlate to better response to treatment or to a molecular subtype of GBM [15]. Acknowledgement We thank Cecil R. Stockard for his excellent immunohistochemical assistance. Supported by American Cancer Society RSG-04-030-01-CCE and NCI P50 CA 097247.
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